Pesticide residue detection data platform based on high resolution mass spectrum, internet and data science, and method for automatically generating detection report

ABSTRACT

Disclosed is a pesticide residue detection data platform based on high resolution mass spectrum, the Internet and data science, and a method for automatically generating a detection report. The platform includes allied laboratories, a detection result database of the allied laboratories, four basic sub-databases, a data collection system and an intelligent data analysis system. The intelligent analysis system reads data according to conditions set by a user, performs various statistical analyses according to a statistical analysis model, generates charts, obtains a comprehensive conclusion, and returns an analysis result to the client ends of the allied laboratories.

TECHNICAL FIELD

This invention presents a method for online tracing and warning ofpesticide residues in agricultural products, particularly refers to thebuilding method of pesticide residue detection data platform andautomatic generation method of detection report based on the ternaryintegration technique which consists of high-resolution massspectrometry, Internet, and data science, which belongs tointerdisciplinary technique.

BACKGROUND ART

At present, in the pesticide residue detection reports published byquality supervision departments, the detection data is mainlyrepresented by data tables and only a few statistical charts. Generationof these reports need long time and have poor timeliness. Moreover,these statistical data and charts are difficult to understand for thepublic, and lack of timely management and early warning functions. Inaddition, as non-target pesticide residue detection techniques areimplemented in a high degree of digitization, informatization andautomation, massive analytical data have been generated, which is also achallenge to traditional data statistics and analysis methods.Therefore, it is urgent to develop a system which can provide theinnovative big data acquisition, transmission, statistics andintelligent analysis. In recent years, with the development ofelectronic information and Internet, new approach and method areprovided for multi-dimensional expression, sharing and analysis of bigdata of pesticide residue detection.

It is necessary to construct a pesticide residue detection data platformbased on interdisciplinary integration of Internet, advancedhigh-resolution mass spectrometry, and data science to realize timelyacquisition, management and intelligent analysis of pesticide residuesdata, generate pesticide residue detection reports automatically in ashort time, provide real-time online service for the traceability andrisk assessment of pesticide residue, realize scientific management anduse of pesticides. However, until now, there is no report on such methodand system.

CONTENTS OF THE INVENTION

The invention presents a ternary interdisciplinary integrationtechnique, which consists of high-resolution mass spectrometry,Internet, and data science, constructs a pesticide residue detectiondata platform and presents an automatic detection report generationmethod. In laboratory union based on Internet and distributed in China,more than 1,200 pesticides commonly used are screening continuously indifferent fruits and vegetables according seasons. Databases areestablished through pesticide residue detection data acquisition toachieve intelligent management and analysis of data, automatic reportgeneration.

The present invention “pesticide residue detection data platformconstruction and automatic detection report generation method based onthe ternary integration technique of a high-resolution massspectrometry, Internet, data science” proposes four major parts: {circlearound (1)} establishing laboratory union and pesticide residuedetection standard methods; {circle around (2)} establishing alaboratory union detection result database and four basic sub-databases;{circle around (3)} establishing a pesticide residue data acquisitionsystem; and {circle around (4)} establishing an intelligent analysissystem of pesticide residue data.

The first part of this invention is to establish laboratory union andstandard pesticide residue detection method. The establishment oflaboratory union refers to establishing laboratory union across thecountry, which are operated under five unified criteria (unifiedsampling, unified sample preparation, unified detection method, unifiedformat data uploading, and unified format statistical analysis report)in a closed system and detect pesticide residues in fruits andvegetables on the market throughout the country all year. The pesticideresidue data detection methods by Liquid Chromatography-Quadrupole-Timeof Flight/Mass Spectrometry (LC-Q-TOF/MS) and GasChromatography-Quadrupole-Time of Flight/Mass Spectrometry (GC-Q-TOF/MS)techniques detect pesticide residues in fruits and vegetables to obtainrelevant raw data of pesticide residues.

The second part of this invention is to establish a laboratory uniondetection result database and four basic sub-databases. Wherein, theunion laboratory detection result database includes names of pesticides,names of agricultural products, sampling time, sampling locations,detection methods, and detection organizations, etc. The four basicsub-databases include a multi-country MRLs database, an agriculturalproduct category database, a pesticide information database, and ageographic information database. The multi-country MRLs databasecontains 241,527 items of relevant MRLs, criteria from differentcounties or regions, such as China, Hong Kong of China, United States,European Union, Japan and CAC. It includes the pesticides, agriculturalproducts, maximum residue limits (MRLs), and the criteria-settingcountries or organizations. The agricultural product category databasemainly contains the category criteria in China, Hong Kong of China, US,EU, Japan, and CAC. It mainly comprises name of agricultural products,primary category, secondary category, and tertiary category, etc. Thepesticide information database includes their basic information such astoxicity, function, chemical composition, prohibition, and derivatives.It specifically comprises name, CAS registry number, toxicityintensities of the pesticides, Whether the pesticides are metaboliccompounds and their metabolic precursors or not, and whether thepesticides are prohibited in the criteria or not. The geographicinformation database covers required geographical scopes, and comprisesdetailed address of all sampling locations in provincial, regional, andcounty-level administrative division, etc.

The third part of this invention is to establish a data acquisitionsystem. Three-layer architecture based on “browser/Web server/databaseserver” comprises a data acquisition module, a data preprocessingmodule, a contamination level judgment module, and a data storagemodule. The browser layer is in the clients of the laboratory union andis an interface for the users to access the system. The Web server layeris located in a data center and is responsible for accessing thedatabases and executing preprocessing logics. The database server islocated in a data center and is responsible for storing and managingvarious kinds of data. The functions of all modules in the acquisitionsystem are as follows: (1) the data acquisition module is responsiblefor acquiring pesticide residue detection results reported by thelaboratory union; (2) the data preprocessing module is responsible forprocessing the reported detection data, including judgment of reporteddata, and supplementation, categorization and merging for theinformation of pesticide, region, and agricultural product category,etc.; (3) the contamination level judgment module is responsible forjudging contamination levels according to the MRLs in differentcountries (or regions, or organizations); (4) the data storage module isresponsible for storing records of final results into the databases.

The fourth part of this inventions is to create an intelligent dataanalysis system, which mainly establish the link and communication amongthe detection result database and the four sorts-databases, and realizesmulti-dimensional cross analysis of sampling locations, pesticides,agricultural products, and contamination levels according to statisticalanalysis models. The system is also based on the three-layerarchitecture of “browser/Web server/database server”, and comprises aparameter setting module, a single item analysis module, a comprehensiveanalysis module, a report generation module, a table generation module,and a prewarning reporting module. The browser layer is in the clientsof the laboratory union and is an interface for the users to access thesystem, set statistical parameters, and download statistical results.The Web server layer is also located in the data center and isresponsible for accessing the databases and executing variousstatistical analysis logics. The database server is located in the datacenter and is responsible for storing and managing various pesticideresidue data. The functions of all modules in the intelligent dataanalysis system are as follows: (1) the parameter setting module isresponsible for providing interface and channel to set parameter for theusers; (2.) the single item analysis module is responsible foraccomplishing 18 individual statistics functions; (3) the comprehensiveanalysis module is responsible for accomplishing 5 comprehensiveanalysis tasks based on individual analysis result; (4) the reportgeneration module is responsible for generating detection reports thatcontain text and charts from the analytical results; (5) the tablegeneration module is responsible for generating various statisticaltables; (6) the warning reporting module provides warning promptsaccording to the analytical results.

BENEFICIAL EFFECTS OF THIS INVENTION

The platform construction and automatic detection report generationmethod presented in this invention provides an efficient and accuratedata analysis platform for pesticide residue data analysis andpre-warning in China. Wherein, the laboratory union and the unitedpesticide residue detection methods could guarantee uniformity,integrality, accuracy, security, and reliability of data. Theestablishment of union laboratory detection result database and fourbasic sub-databases provides basis for pesticide residue detection dataanalysis and contamination level judgment. The presented pesticideresidue data acquisition system realizes automatic uploading ofdetection results, data preprocessing, and contamination level judgment.Based on the above, we established a national pesticide residuedetection result database. The presented intelligent pesticide residuedata analysis system establishes the link and communication among theraw detection data and the four basic sub-databases, realizes individualand comprehensive statistics and analysis of multi-dimensional pesticideresidue data, and automatically generates detection result reports thatcontain text and charts. By “one-button download”, the detection resultreport could be generated within 30 minutes, which can't be realizedwith traditional statistical methods.

Compared with the existing manual reports, the detection reportsgenerated method in this invention not only has high accuracy, highspeed, and diverse judgment criteria, but also has flexible statisticalrange and various analysis methods. The platform and method in thisinvention realize the automation of online data acquisition, resultjudgment, statistical analysis, and report generation. They greatlyimprove the depth, accuracy and efficiency of data analysis, and are ofgreat practical significance and commercial application value.

DESCRIPTION OF DRAWINGS

FIG. 1 shows the Internet pesticide residue detection data analysisplatform across China;

FIG. 2 shows the pesticide residue detection data acquisition system;

FIG. 3 shows the intelligent pesticide residue detection data analysissystem;

FIG. 4 shows an automatically generated pesticide residue detectionreport;

FIG. 5 shows a parameter selection interface for automatic export ofpesticide residue detection report;

FIG. 6 shows the five-level tree structure of administrative divisionsfor pesticide residue detection reports;

FIG. 7 shows the content of a pesticide residue detection report;

FIG. 8 shows the detection rates of pesticide residues in fruits andvegetables from 31 provincial capitals/municipalities markets in2012-2015;

FIG. 9 shows measurement of sample safety level according to the MRLstandards of several countries, regions, or international organizations;

FIG. 10 shows the toxicity categories and percentages of detectedpesticides;

FIG. 11 shows the species and frequencies of pesticides exceedingCAC-MRLs.

EMBODIMENTS

This invention will be presented in detail with reference to theaccompanying drawings and embodiments.

The Internet-based national big data technical platform of pesticideresidue detection is shown in FIG. 1. It comprises four main parts:{circle around (1)} more than 30 Internet-based laboratories across thecountry; {circle around (2)} a union laboratory- detection resultdatabase and four basic sub-databases (a multi-country MRLs database, anagricultural product category database, a basic information of pesticidedatabase, and a geographic information database); {circle around (3)} apesticide residue data acquisition system; {circle around (4)} anintelligent pesticide residue analysis system. The last two partsconstitute a data processing center. The working principle of theplatform is shown below. The raw pesticide residue detection results arereported from clients in the union laboratory distributed in the countryto the acquisition system via Internet, as shown in FIG. 2. Theacquisition system carries out the judgment of the contamination levelsby data acquisition, information supplementation, derivative informationmerging, toxicity analysis, and according to the MRL standards indifferent countries, records the result, and stores the records into thedetection result database. The intelligent analysis system sets andreads the data according to the criteria set by the users, performsstatistical data analyses one by one according to statistical analysismodels, generates charts, draws general conclusions and createsdetection reports. Finally, it returns the analytical results to theclients in the union laboratory for viewing and downloading, as shown inFIG. 1.

Table 1 shows the raw detection result database and four basicsub-databases (multi-country MRLs database, agricultural productcategory database, basic information of pesticide database, andgeographic information database) established in more than 30laboratories across the country. An associated data storage and querymodel established based on “MRL standards in severalcountries-categories of agricultural products-properties of more thanone thousand pesticides” is proposed. Thus, linked basic pesticideresidue data access and invocation is realized, and a standard basis forjudgment of the pesticide residue detection results is provided.

A pesticide residue data acquisition system is designed as shows in FIG.2, and a national pesticide residue detection result database isestablished. A data integration and processing model consisting of “dataacquisition-information supplementation-derivativeconsolidation-prohibited pesticide handling-contamination leveljudgment” is presented, which realizes quick online acquisition andmerging of pesticide residue detection result data, accurate judgment ofthe data according to MRLs from several countries and dynamic additionand real-time update of the pesticide residue detection result databaseis realized, and provides scientific data for decision-making of foodsafety in the country. As shown in FIG. 2, the pesticide residuedetection data acquisition system employs three-layer architecture basedon browser/server. The laboratory union are operated under five unifiedspecifications (unified sampling, unified sample preparation, unifieddetection, unified format data uploading, and unified format statisticalanalysis report) in a closed system, utilizes LiquidChromatography-Quadrupole-Time of Flight/Mass Spectrometry (LC-Q-TOF/MS)and Gas Chromatography-Quadrupole-Time of Flight/Mass Spectrometry(GC-Q-TOF/MS) techniques to report pesticide residue detection data thatis detected in fruits and vegetables, which can fully guarantee theuniformity, integrality, accuracy, security, and reliability of data.The raw detection result data is acquired with ASP.NET technique tosupply the information on pesticides, regions, and agricultural productcategories merge derivatives and manage pesticide toxicitycategorization. The result is judged contamination level according tothe MRL criterion of the countries or regions or organizations andstored the generated records of results in the detection resultdatabase.

An intelligent pesticide residue detection data analysis system isestablished as shown in FIG. 3. The intelligent analysis systemcomprises a presentation layer, a business layer, an access layer, and adata layer. The data layer consists of the detection result database,the four basic sub-databases, and relevant files and is configured toprovide database and file services. The access layer accesses the datain the databases via a database access component and provides the datato the business layer. The business layer realizes multi-dimensionalstatistical analysis of sampling locations, pesticides, andcontamination levels according to the statistical analysis models. Thepresentation layer provides various intelligent analysis reports thatcontain text and charts according to the criterion set by a client. Anonline custom mode is established in the present invention to supportthe users to select and filter the statistical data autonomously, tohighlight the data of interest or key data. Meanwhile it supports theuser to customize the report type and range, to improve datapresentation and big data analysis capability. It is realized thatmulti-dimensional automatic statistics of 20 pesticide residue indicesincluding agricultural products, pesticides, regions, and MRLs indifferent countries, as shown in Table 2. Wherein, 31 different tablesand 38 different figures can be generated automatically, andcomprehensive assessment and warning information can be generatedautomatically according to the statistical results. Finally, a pesticideresidue detection report that contains text and charts can be generatedautomatically within 30 minutes by “one-button download”, as shown inFIG. 4.

FIG. 4 shows the result of multi-discipline multi-element pesticideresidue big data integration technique based on ternaryinterdisciplinary integration technique of high-resolution massspectrometry, Internet, and data science. “One-button download” isrealized, and a detection report that contains texts and charts can begenerated within 30 minutes. The pesticide residue detection reportreflects 20 regular characteristics of pesticide residues in more than150 species of fruits and vegetables in 18 categories in 31 provincialcapitals/municipalities in the country, as shown in Table 2.

TABLE 2 20 regular characteristics of pesticide residues discoveredthrough big data statistical analysis (1) It is found that pesticideresidues exist almost in most fruits and vegetables from 31 provincialcapitals/municipalities. The pesticide residue detection rate is 39%-88%(LC-Q-TOFMS) or 54%-97% (GC-Q-TOFMS). (2) Altogether 517 pesticides aredetected in more than 150 species of fruits and vegetables in 18categories (wherein, 93 pesticides are detected with both techniques) inChina; (3) It is found that the pass rate of pesticide residues infruits and vegetables from 31 provincial capitals/municipalities is96.3%-98.7%, which means that the safety level is assured essentially;(4) The regular characteristics of pesticide residue detection levels(1-5, 5-10, 10-100, 100-1,000, greater than 1,000 μg/kg) in our countryare discovered (by comparison with MRLs in China, EU, and Japan, etc.);(5) The regular characteristics of detected pesticide species inindividual samples (not found, 1 species, 2-5 species, 6-10 species,more than 10 species) in our country are discovered; (6) The regularcharacteristics of detected pesticide species in the same category ofsamples (not detected, 1 species, 2-5 species, 6-10 species, more than10 species) in our country are discovered; (7) The regularcharacteristics of pesticide functions (insecticides, bactericides,herbicides, plant growth regulators, synergistic agents, and otherspecies, and their proportions) in our country are discovered; (8) Theregular characteristics of toxicity of pesticides in our country(species of pesticides of slightly toxic, low toxic, slightly low toxic,moderately toxic, highly toxic, vitally toxic, and prohibited, and theirproportions) are discovered; (9) The order of pesticide species detectedthroughout the country and in the provincial capitals and the order offrequencies of detection are discovered, revealing the differences inpesticide application in fruits and vegetables among different regionsthroughout the country; (10) The order of safety (“exceeding”, “detectedbut not exceeding”, “not detected”) of the detected pesticidesthroughout the country and in the provincial capitals is discovered (bycomparison with MRL standards in China, EU, and Japan); (11) It is foundthat the MRLs in China is confronted with a challenge of lower level andless quantity when compared with MRLs in developed countries such asUSA, EU, and Japan; (12) It is found that only 40% of the massiveresidue data in the general investigation is used according to the ChinaMRLs, while the application ratio of the data is as high as 95% or abovein EU and Japan; consequently. (13) Top 10 species of fruits andvegetables in which the quantities of pesticide species are the largestand the order of follow-tap fruits and vegetables are discovered; it isfound that the common fruits and vegetables are contaminated severely.(14) Top ten species of fruits and vegetables in which the averagedetected frequency of pesticides is the highest and the order of thefollow-up fruits and vegetables are discovered; (15) The species ofhighly toxic, vitally toxic, and prohibited pesticides and the detectionfrequencies are discovered; (16) Top ten fruits and vegetables in whichthe quantities of highly toxic, vitally toxic, and prohibited pesticidesare the largest and the order of follow-up fruits and vegetables arediscovered; (17) Top ten fruits and vegetables in which the detectedfrequency of highly toxic, vitally toxic, and prohibited pesticides isthe highest and the order of follow-up fruits and vegetables arediscovered; (18) The general characteristics of and the differences inthe existence of pesticides in the commercial fruits and vegetables in31 provincial capitals/municipalities are discovered; (19) Thecharacteristics of and the differences in the pesticides detected at thesampling locations in 31 provincial capitals/ municipalities arediscovered; (20) The characteristics of and the differences in the useof pesticides in 31 provincial capitals/municipalities are discovered.

The download parameters of pesticide residue detection result report areshown in FIG. 5. The sampling period and type can be selected freely.One or more administrative divisions can be selected at will (afive-level architecture of “national-regional-provincial-city-county”can be realized) as shown in FIG. 6. User can select the type of thetesting instrument and export the body part or the annexed tables of thereport as required. The content of the body part of a local reportconsists of 5 chapters, as shown in FIG. 7. The report of detectionresult includes various charts to help user visually understandstatistic result. For example, reflecting the detection rates ofpesticide residues in fruits and vegetables from 31 provincialcapitals/municipalities (see FIG. 8). Pie charts that reflect the safetylevels of the detected samples, as shown in FIG. 9. Toxicity categoriesof detected pesticides and their proportions, as shown in FIG. 10. Andbar charts that are used for out-of-specification analysis of specificsamples (sec FIG. 11), etc. There are 20 annexed tables which could beselected in the report. They record the raw detection results and detailstatistics of concentration distribution, contamination levels, andout-of-specification (MRLs) of detected pesticide residues.

A report may contain words ranging from tens of thousands of words tohundreds of thousands of words depending on the data size, and the bodypart and the annexed tables may contain text and charts. Such a reportmay be generated and downloaded by “one-button download” within 30minutes. Thus, the analysis and reporting ability to the massivepesticide residue data is greatly improved. Besides, the automaticreporting system further supports customization and extension of reportstructure and content.

Example of analysis report: the pesticide residue detection resultdatabase now contains 13.74 million detection data items of 22,368batches samples of more than 140 specifies of fruits and vegetables from638 sampling spots in 31 provincial capitals/municipalities (including284 counties) in the country, which is stored in 10 laboratories in thecountry, 145 million high-resolution mass spectra are collected, andpesticide residue detection reports containing 25 million words in totalare formed.

The basic information of pesticide residues in fruits and vegetablesfrom 31 provincial capitals/municipalities in the country has beeninvestigated preliminarily, as shown in FIG. 8, Tables 3 and 4. Thefurther general investigation of the basic situation of pesticideresidues in fruits and vegetables from Beijing, Tianjin, and Hebei in2016 is similar to that of pesticide residues in fruits and vegetablesin 31 provincial capitals/municipalities in 2012-2015.

TABLE 3 Basic information of pesticide residues in fruits and vegetablesfrom 31 provincial capitals/municipalities (2012-2015) Item LC-Q-TOF/MSGC-Q-TOF/MS Detected pesticide 174/25448 343/20418 species/frequencyRange of pesticide residue 39.3%-88.0% 28.6%-100% detection rate Totalnumber 424 Total number of 93 species of pesticide species/ pesticidespecies species/frequencies 45,866 detected by both detected timestechniques by both techniques

TABLE 4 Basic information of pesticide residues in fruits and vegetablesfrom Beijing, Tianjin, and Hebei (2016) Item LC-Q-TOF/MS GC-Q-TOF/MSDetected pesticide 161/9724 197/9834 species/frequency Range ofpesticide residue 20.0%-100.0% 50.0%-100.0% detection rate Total number279 Total number of 56 species of pesticide species/ pesticide speciesspecies/frequencies 19,558 detected by both detected times techniques byboth techniques

It is shown in Table 3 that in the 22,368 samples from 31 provincialcapitals/municipalities in 2012-2015, totally 517 pesticides weredetected (wherein, 93 pesticides were detected by both techniques), andthe detected frequency was 45,866 times. It is listed in Table 4 that inthe 10,190 samples from Beijing, Tianjin, and Hebei in 2016, totally 227pesticides were detected, and the detected frequency was 19,558 times.It is found in the big data analysis for the general investigation from31 provincial capitals/municipalities in 2012-2015 and the generalinvestigation from Beijing, Tianjin, and Hebei in 2016 that the safetylevel of commercial fruits and vegetables in China was essentiallyassured, at 97% or above pass rate according to the China MRL standards.However, the pesticide residue problem was still severe. It is found inthe big data statistical analysis: {circle around (1)} highly toxic orvitally toxic pesticides (e.g., Carbofuran, Isocarbophos, andMethidathion) and prohibited pesticides (e.g., Thimet, Ethoprophos) werestill detected frequently, and the detection frequency is 5.5% of thetotal detection frequency; {circle around (2)} there are about 19%samples in which the pesticide residues were exceeding MRLs ; {circlearound (3)} there are about 0.7%; individual samples in which more than10 pesticide residues were found {circle around (4)} the quantity ofpesticide residue species detected in single specie of fruits andvegetables was 30 or more, and was even about 100 pesticides at themost; {circle around (5)} The detection rates of pesticide residues incommon fruits (grape, apple, pear and peach) and vegetables (celery,tomato, cucumber and sweet pepper) were high, and the phenomena ofexceeding MRLs were severe, shown in Tables 5 and 6; {circle around (6)}comparing with the MRL standards in advanced countries, the pesticideresidue MRLs in China are confronted with a challenge of lower quantityand lower threshold. For example, in the 9,834 detected times ofpesticide residues in the general investigation (GC-Q-TOF/MS) fromBeijing, Tianjin, and Hebei in 2016, there are only 2,233 correspondingMRL items in the China MRL standards, which is 22.7%. China MRLstandards are the lowest among all of the 6 MRL standards, which aremuch lower than the MRL standards in EU and Japan.

TABLE 5 Detection results of pesticide residues in 4 types of fruits(grape, apple, pear and peach) and 4 types of vegetables (celery,tomato, cucumber, and sweet pepper) LC-Q-TOF/MS GC-Q-TOF/MS NumberNumber of Number of of pesticide Number of pesticide samples species inTotal samples in species in Total in which which number which Pesticidewhich number pesticides Pesticide pesticides of pesticides detectionpesticides of are detection are Sample samples are detected rate, % aredetected Sample samples detected rate, % detected Grape 411 367 89.3 75Grape 389 316 85.6 81 Apple 628 579 92.2 61 Peach 279 234 83.9 83 Pear574 397 69.2 52 Pear 437 349 79.9 91 Celery 537 479 89.2 87 Celery 353341 96.6 132 Tomato 621 547 88.1 81 Cucumber 343 381 87.8 112 Cucumber591 548 92.7 76 Sweet 369 292 79.1 104 pepper

TABLE 6 MRL analysis of three categories of pesticide residues in 4types of fruits (grape, apple, pear and peach) and 4 types of vegetables(celery, tomato, cucumber, and sweet pepper) LC-Q-TOF/MS GC-Q-TOF/MSNumber of out-of-speci- Number of Number of Number of Number of Numberof fication out-of-speci- out-of-speci- out-of-speci- out-of-speci-out-of-speci- pesticides fication fication fication fication ficationaccording pesticides pesticides pesticides pesticides pesticides toChina according to according to according to according to according toMRL EU MRL Japan MRL China MRL EU MRL Japan MRL Sample standardsstandards standards Sample standards standards standards Grape 9 24 25Grape 3 24 33 Apple 3 17 11 Peach 3 23 30 Pear 4 11 9 Pear 2 24 33Celery 7 45 36 Celery 8 69 88 Tomato 5 21 21 Cucumber 5 32 37 Cucumber 822 22 Sweet 2 19 37 pepper

The above detailed description is provided only to describe somefeasible embodiments of the present invention rather than to limit theprotection scope of the present invention. Any equivalent embodiment ormodification implemented without departing from the spirit of thepresent invention shall be deemed as falling in the protection scope ofthe present invention.

1. A pesticide residue detection data platform based on high-resolutionmass spectrometry, Internet, and data science includes laboratory union,a union laboratory detection result database and four basicsub-databases, a data acquisition system, and an intelligent dataanalysis system, wherein, the laboratory union refers to severalstandard laboratories established across the country, which are operatedunder five unified specifications in a closed system and detectpesticide residues in fruits and vegetables on the market throughout thecountry all year; the union laboratory detection result databasecontains names of pesticides, names of agricultural products, samplingtimes, sampling locations, detection methods, and detectionorganizations; the four basic sub-databases are a multi-countryMRL(maximum residue limit) database, an agricultural product categorydatabase, a pesticide information database, and a geographic informationdatabase; the data acquisition system realizes automatic uploading ofdetection result, data preprocessing, and contamination level judgment,to establish a national pesticide residue detection result database; thedata acquisition system comprises a data acquisition module, a datapreprocessing module, a contamination level judgment module, and a datastorage module; the data acquisition module is responsible for acquiringpesticide residue detection results reported by the union laboratories;the data preprocessing module is responsible for processing the reporteddetection data, including judgment of reported data, andsupplementation, categorization, and merging of information ofpesticide, region, and agricultural product category; the contaminationlevel judgment module is responsible for judging contamination levelsaccording to the MRLs of different countries or regional organizations;the data storage module is responsible for storing records of finalresults into the databases; the intelligent data analysis systemrealizes link and communication among the detection result database andthe four basic sub-databases, accomplishes multi-dimensional crossanalysis of sampling locations, pesticides, agricultural products, andcontamination levels according to statistical analysis models, sets andreads data and then carries out statistical analyses according to thestatistical analysis models on the criteria set by the users, generatescharts, draws comprehensive conclusions, provides detection reports, andreturns the analytical results to clients in the union laboratories forviewing and downloading; the intelligent data analysis system comprisesa parameter setting module, an single item analysis module, acomprehensive analysis module, a report generation module, an annexedtable generation module, and a warning reporting module; the parametersetting module is responsible for providing interface and channel ofparameter set by the users; the single item analysis module isresponsible for accomplishing itemized statistics of several items; thecomprehensive analysis module is responsible for accomplishingcomprehensive analysis of several items on the results of single itemanalysis; the report generation module is responsible for generatingdetection reports that contain text and charts from the analyticalresults; the annexed table generation module is responsible forgenerating various statistical tables; the warning reporting moduleprovides warning prompts according to the analytical results; theintelligent data analysis system is specifically implemented asincluding a presentation layer, a business layer, an access layer, and adata layer; the data layer consists of the detection result database,the four basic sub-databases, and relevant files, and is configured toprovide database and file services; the access layer accesses the datain the databases via a database access component and provides the datato the business layer; the business layer realizes multi-dimensionalstatistical analysis of sampling locations, pesticides, andcontamination levels according to the statistical analysis models; thepresentation layer provides various intelligent analysis reports thatcontain text and charts according to several criteria set by the client.2. The pesticide residue detection data platform based onhigh-resolution mass spectrometry, Internet, and data science accordingto claim 1, wherein, the multi-country MRLs database includes 241,527items of MRL standard items from China MRL, Hong Kong of China MRL, USMRL, EU MRL, Japan MRL, and CAC MRL standards, targeted pesticides,agricultural products, permitted MRLs, and the standard establishmentcountries/regions/organizations.
 3. The pesticide residue detection dataplatform based on high-resolution mass spectrometry, Internet, and datascience according to claim 1, wherein, the agricultural product categorydatabase comprises standards of China categorization, Hong Kong of Chinacategorization, US categorization, EU categorization, Japancategorization, and CAC categorization.
 4. The pesticide residuedetection data platform based on high-resolution mass spectrometry,Internet, and data science according to claim 3, wherein, theagricultural product category database specifically comprises names ofagricultural products, primary category information, secondary categoryinformation, and tertiary category information.
 5. The pesticide residuedetection data platform based on high-resolution mass spectrometry,Internet, and data science according to claim 1, wherein, the pesticideinformation database contains basic information, toxicity information,function information, chemical composition, prohibition information, andderivative information.
 6. The pesticide residue detection data platformbased on high-resolution mass spectrometry, Internet, and data scienceaccording to claim 5, wherein, the pesticide information databasespecifically comprises names of all detected pesticides, CAS registrynumber of the pesticides, toxicity intensities of the pesticides,whether the pesticides are metabolic products and their metabolicprecursors or not, and whether the pesticides are prohibited in thestandards or not.
 7. The pesticide residue detection data platform basedon high-resolution mass spectrometry, Internet, and data scienceaccording to claim 1, wherein, the geographic information databasecovers required geographical scopes, and comprises detailed address ofall sampling locations in provincial administrative division, regionaladministrative division, and county-level administrative division. 8.The pesticide residue detection data platform based on high-resolutionmass spectrometry, Internet, and data science according to claim 1,wherein, the data acquisition system is implemented on the basis ofthree-layer architecture consisting of browsers, a Web server, and adatabase server, wherein the browsers are located in the clients in theunion laboratories and are interfaces for the users to access thesystem; the Web server is located in a data center and is responsiblefor accessing the databases and executing preprocessing logics; thedatabase server is located in the data center and is responsible forstoring and managing various pesticide residue data.
 9. The pesticideresidue detection data platform based on high-resolution massspectrometry, Internet, and data science according to claim 1, wherein,the intelligent data analysis system is implemented on the basis ofthree-layer architecture consisting of browsers, a Web server, and adatabase server; the browsers are located in the clients in the unionlaboratories throughout the country, and are interfaces for the users toaccess the system, set statistical parameters, and download statisticalresults; the Web server is also located in the data center and isresponsible for accessing the databases and executing variousstatistical analysis logics; the database server is located in the datacenter and is responsible for storing and managing various pesticideresidue data.
 10. The pesticide residue detection data platform based onhigh-resolution mass spectrometry, Internet, and data science accordingto claim 1, wherein, the five unified specifications include unifiedsampling, unified sample preparation, unified detection method, unifiedformat data uploading, and unified format statistical analysis report.11. An automatic pesticide residue detection report generation methodusing the pesticide residue detection data platform based onhigh-resolution mass spectrometry, Internet, and data science accordingto claim 1, comprising: reporting raw pesticide residue detectionresults to the data acquisition system from the clients in the unionlaboratories distributed in the country over Internet; the dataacquisition system carries out the judgment of contamination levels bydata acquisition, information supplementation, derivative informationmerging, and toxicity analysis, and according to the MRL standards indifferent countries, records the results, and stores the records ofresults into the detection result database; the intelligent analysissystem sets and reads the data according to the criteria set by theusers, then performs statistical analyses of the data one by oneaccording to statistical analysis models, generates charts, drawscomprehensive conclusions, generates detection reports, and returns theanalytical results to the clients in the union laboratories.
 12. Theautomatic pesticide residue detection report generation method accordingto claim 11, wherein, the union laboratories detect pesticide residuesby Liquid Chromatography-Quadrupole-Time of Flight/Mass Spectrometry(LC-Q-TOF/MS) and Gas Chromatography-Quadrupole-Time of Flight/MassSpectrometry (GC-Q-TOF/MS) and report pesticide residue detection datathat is detected all year.
 13. The automatic pesticide residue detectionreport generation method according to claim 11, further comprising:setting up an online custom mode in the intelligent analysis system tosupport the users to select and filter the statistical dataautonomously, to highlight the data of interest or key data, and tosupport the users to customize the type and range of report.